Understand
Explain the concept and its limits in your own words.
ACADEMY
Academy: From Curiosity to Evidence-Based Making
Fourteen learning pathways turn coding, research, safety, design, science, mathematics and communication into real products, experiments, reports and quiz evidence.
HOW IT WORKS
Read, apply, test, explain and rebuild
Success in the Academy is not finishing pages quickly. Learners explain a concept without the source, apply it in a new example, inspect a failed first attempt and build a second version from feedback.
Each lesson compass shows the central question, evidence to produce, common trap and next step. Module centres present lessons through prerequisites, practice and projects rather than as random cards.
No learner account is required and quiz results are not sent to a server. Save code, test tables, reports and project journals on your own device to build a personal learning portfolio.
Apply
Produce code, an experiment, a design, a table or research evidence.
Check
Make errors visible through quizzes, tests, measurement and user feedback.
Rebuild
Record what changed in the second version and why.
01
Code and Build
Robotics & Coding
Connects algorithms, Scratch, Python, web, electronics, micro:bit, Arduino, sensors, actuators, robotic systems, data and project work in one production path. Evidence: Working code, circuit diagram, input–process–output tests, error log and project versions.
Open pathway →Git, Open Source and Teamwork
Connects repositories, commits, branches, diffs, merges, issues, pull requests, licences and open-source contribution to real teamwork. Evidence: A readable commit history, small branch, reviewable pull request and contribution record.
Open pathway →Creative Coding and Game Design
Combines patterns, animation, physics, events, state machines, level design, sound, user testing and accessibility in a creative-coding cycle. Evidence: A playable prototype, design intention, user test, error log and second version.
Open pathway →Engineering Design and 3D Making
Combines problem definition, constraints, criteria, sketching, 3D modelling, tolerance, materials, print orientation, testing and iteration in one design cycle. Evidence: A requirements table, alternative sketches, dimensioned model, test report and version comparison.
Open pathway →02
Understand and Investigate
Information, Media and Research Literacy
Teaches learners to examine origin, method, independent verification and context rather than appearance. Evidence: A claim–source–evidence–uncertainty table and verification trail.
Open pathway →Mathematics for Coding and Robotics
Connects mathematical concepts to sensor scaling, motor control, position, turns, graphs, uncertainty and calibration rather than isolated exercises. Evidence: Variables, unit checks, worked calculation, code equivalent and measurement verification.
Open pathway →The Science Behind Robots
Examines robot behaviour through physical models, controlled experiments, measurement uncertainty and energy calculations—not code alone. Evidence: A controlled experiment, measurement table, graph and physical explanation.
Open pathway →Learning How to Learn
Turns goals, retrieval practice, spaced review, deliberate practice, error logs, help seeking and focus systems into a personal learning cycle. Evidence: A scheduled review plan, closed-book retrieval record, error log and transfer task.
Open pathway →03
Stay Safe and Responsible
Digital Safety and Mindful Internet Use
Turns passwords, MFA, updates, backups, phishing, social engineering, privacy and cyberbullying into behaviour-based security habits. Evidence: A personal threat model, incident record, recovery plan and family safety checklist.
Open pathway →Responsible Artificial Intelligence
Connects problem definition, data quality, hallucination, human oversight, risk levels, personal data, deepfakes and model error analysis in a responsible-use cycle. Evidence: A responsible-AI canvas covering purpose, data, output, affected people, errors, oversight and appeal.
Open pathway →Technology and the Planet
Connects energy-efficient code, low-power hardware, environmental sensors, reuse, modular design, e-waste and carbon calculations to measurable sustainability decisions. Evidence: A bounded life-cycle map, energy measurement, materials list and improvement comparison.
Open pathway →04
Design and Communicate
User Experience and Accessible Design
Produces user observation, task flow, prototypes, tests, error-message and accessibility evidence instead of relying on assumptions. Evidence: A task-based usability test, issue log, accessibility check and second prototype.
Open pathway →Technical Communication and Technology English
Turns summaries, scope, materials and software lists, diagrams, code explanations, test tables, posters, presentations, references and technical English into reproducible communication. Evidence: A versioned documentation package and a rebuild test by another person.
Open pathway →Sport, Data and Technology
Connects accelerometers, steps, reaction, heart rate, sampling, graphs, personal progress and data privacy to safe sports-technology projects. Evidence: A measurement protocol, device limitations, data permission, graph and fair-comparison note.
Open pathway →PORTFOLIO
Six artefacts to keep each month
The monthly portfolio is not a score competition. Keeping older versions shows how learning changed. Never include private data, real passwords, unauthorised photos or another person’s account.
- A concept explanation in your own words
- A working or measured application
- One correction from an error log
- Feedback from another person
- A source and licence record
- A second version and next question
A MONTH FROM NOW
Academy: what should you be able to show after one month?
Fourteen learning pathways turn coding, research, safety, design, science, mathematics and communication into real products, experiments, reports and quiz evidence. The first visit is therefore not about opening every card; it is about making the starting point visible. Replace “I know this” with a short explanation, a small application and one edge case.
In Week 1 work with suitable items among Information, Media and Research Literacy, Digital Safety and Mindful Internet Use, Git, Open Source and Teamwork, Mathematics for Coding and Robotics. For each, write three lines: my first prediction, what the application showed and what I changed. For code or circuits, record input, output and failure behaviour; for research or design, record criteria, evidence and feedback.
In Weeks 2 and 3 do not merely consume the sequence. Use one lesson concept in another lesson: identify a shared variable, method or safety limit among Technical Communication and Technology English, Technology and the Planet, Creative Coding and Game Design, Learning How to Learn. Demonstrate the connection through a table, diagram, sample data or a second prototype.
In Week 4 repeat the same task while consulting sources less often. Compare first and second versions for result, method, errors, explainability and safety. Speed alone is not progress; you should explain why the result is sound and transfer the idea to a new situation.
When returning to the centre, do not delete older records. Add a date, changed source or tool version, new error and next mini trial. This version history makes the difference between short-lived familiarity and durable, transferable learning visible.
- Starting evidence: a 90-second explanation in your own words
- Practice evidence: tests with safe real or fictional inputs
- Error evidence: condition–expected–actual–correction record
- Transfer evidence: use the concept in another lesson or project
- Source evidence: version, date, licence and technical limit
- Close: next question and review date
MICRO QUIZ
Test the reasoning behind the module
1. What does finishing a lesson not prove?
It does not by itself prove transfer to a new situation or the ability to correct errors.
2. Why keep the first version?
To compare what changed after feedback.
3. Are quiz results sent to a server?
No; they are designed to remain in the local browser.
4. Which data should not enter a portfolio?
Real passwords, private messages, confidential documents or unauthorised personal data.
5. Which pathway should be chosen first?
The one producing the smallest relevant evidence while meeting safe prerequisites.
SOURCE MAP
Verify technical details in primary sources
Python
Check version, method, safety or accessibility limits in the official source.
Open →Arduino
Check version, method, safety or accessibility limits in the official source.
Open →WCAG 2.2
Check version, method, safety or accessibility limits in the official source.
Open →NIST AI RMF
Check version, method, safety or accessibility limits in the official source.
Open →LEARNING ENTRIES
Start by time and the evidence you want to produce
The right Academy start is not the most popular topic; it is the time available and the artefact you want to produce.
10 minutes
Read a concept, solve one example and write a three-sentence note.
Open short session →30 minutes
Create a small code, source check or design trial.
Open practice →1–2 hours
Produce a prototype, research file or data artefact.
Open project workshop →30 days
Build a lesson–practice–quiz–second-version loop.
Choose a route →Review mistakes
Link quiz results back to lessons and projects.
Open progress →Free resources
Continue the route through free institutions and platforms.
Open resources →